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Short Courses

SIL2021 organizes short courses to share expertise of SIL scientists with broad society members and students. For this time, all short course will be held online to invite broader participants. Short courses will cover diverse topics in the limnological studies. And these practical courses will represent a unique opportunity to foster ideas and collaborations among international limnologists. During short course, student volunteers will be holding opportunity to assist and get in close touch with the specialists. Therefore, this short course not just give the techniques, but also networks with the limnologists who are interested in.

During SIL2021, all short courses will be held free of charge for SIL members in order to promote technical exchange during conference and encourage participation of young limnologists. If you are not a SIL member, please check SIL membership page to join* (https://limnology.org/sil-membership-types/; special rates are provided for student and early career members). Supporting fund for short courses during SIL2021 is organized by KSL (The Korean Society of Limnology).

Pre-registration for short course is needed at the SIL2021 registration page (check options for short course). As participation capacity can be limited depending on the short course topics, registration for the short course participation may close earlier if all seats are taken. Early registration for the interested topic is recommended.

Short Course Registration

- Short courses require advance sign-up during congress registration.
- The number of participants is limited.
- Only registered SIL members (student or regular) could participate in short courses.
  Please check the special SIL membership rate for the student and early careers(https://limnology.org/sil-membership-types/).
* Student SIL membership fee: USD 15-30 for three years($ 5-10 per year)

Short Course Date and Location

  • Date
    August 22 (Sunday), 2021
  • Participation type
    online video meeting (e.g., Zoom etc.)

* Private meeting link, Lecture/Practice materials will be sent to registered participants by their e-mail.

List of Short Courses

No Titles Time Schedule
(August 22, GMT+9)

SC-01

Chironomidae identification methodology

13:00-21:00

SC-02

How to use stable isotopes in ecology and environmental study?

10:30-16:00

SC-03

Long-term data analysis based on R

16:00-20:30

SC-04

Ecological Applications of Bayesian Statistics -- with R and Stan

08:00-15:00

SC-05

Spatial and individual-based models for ecological monitoring through simulation

17:00-23:00

SC-06

Evolutionary Algorithms: Predictive and Explanatory Modelling of Freshwater Ecosystems by Learning from Ecological Data 10:00-14:00

SC-07

Aquatic Ecosystem Modeling (PCLake and GLM-AED2)

19:00-23:00

SC-08

From data to visualization and storytelling

16:00-22:00
* Short course time in Seoul(GMT+9)
* Please make sure to properly convert it for your Time Zone:
   click the clock in front of each course start time to access a relative time conversion table.

Time Table

Time SC-01 SC-02 SC-03 SC-04 SC-05 SC-06 SC-07 SC-08
08:00-09:00 SC-04
09:00-10:00 SC-04
10:00-11:00 SC-02
(10:30)
SC-04 SC-06
11:00-12:00 SC-02 SC-04 SC-06
12:00-13:00 SC-02 SC-04 SC-06
13:00-14:00 SC-01 SC-02 SC-04 SC-06
14:00-15:00 SC-01 SC-02 SC-04
15:00-16:00 SC-01 SC-02
16:00-17:00 SC-01 SC-03 SC-08
17:00-18:00 SC-01 SC-03 SC-05 SC-08
18:00-19:00 SC-01 SC-03 SC-05 SC-08
19:00-20:00 SC-01 SC-03 SC-05 SC-07* SC-08
20:00-21:00 SC-01 SC-03 SC-05 SC-07* SC-08
21:00-22:00 SC-05 SC-07* SC-08
22:00-23:00 SC-05 SC-07*
23:00-24:00
*SC-07 : 2 group-parallel workshop

Short Course Descriptions

  * Please click the course title to see the detailed description.
  • Organizer/Lecturer:

    - Dr. Djuradj Milošević (University of Niš)
    - Dr. Dubravka Čerba (University of J. J. Strossmayer)


    Course description:

    Members of the Chironomidae family (Diptera) are one of the dominant and most diverse aquatic macroinvertebrate groups and also probably the most widely distributed insect family in freshwaters (all types of water bodies, all continents) and can even be found in marine habitats. Chironomid larvae play an essential role in food webs and ecosystem functioning, as they are ecosystem engineers, good colonizers and represent a main link between different trophic levels within aquatic ecosystems. Furthermore, chironomid larvae are excellent indicators of water quality, representing a key group in most national bioassessment and monitoring programs. It can be time consuming and very difficult to identify this group beyond subfamily level and so, to gain the knowledge how to do it easier and correctly, we will provide for the attendees - lectures, videos and explanations on methodology of larvae handling and identification and specific morphological traits of subfamilies, tribes and certain genera, important for their identification.


    Course objectives:

    To master the techniques necessary for chironomid identification conducting different activities: introduction lectures, demonstrations and practical work, suitable for participants with different levels of foreknowledge.


    Recommended reading or materials

    www.pmf.ni.ac.rs/ewcim/


    Maximum number of participants: 10 people
  • Organizer/Lecturer:

    - Dr. Kyung-Hoon Shin (Hanyang University)
    - Dr. Yoshito Chikaraishi (Hokkaido University)
    - Dr. Liew Jia Huan (The University of Hon Kong)


    Course description and objectives:

    Stable isotope analysis has been successfully carried out to identify the energy flow in natural ecosystem, and to trace the pollution source in various environments over the last a few years. Current short course lecture introduces fundamental theory and typical cases studies. It will be very nice training opportunity for graduate students and younger scientists as well as researchers who are interested in how to use stable isotopes in their ecological and environmental researches. In addition, recent progresses of stable isotope techniques such as the use of multi-elements isotopes and compound specific isotope analysis (CSIA) are introduced to provide new insights in ecology and environment study, and also improved understanding of stable isotopes applications


    Recommended reading or materials

    - Text book "Stable Isotope Ecology written by Brian Fry in 2006" and "Stable Isotopes in Ecology and Environmental Science   edited by Robert Michener and Kate Lajtha, in 2007"


    Maximum number of participants: 30 people
  • Organizer/Lecturer:

    - Dr. Jianming Deng (Nanjing Institute of Geography and Limnology, CAS)
    - Dr. Hyunbin Jo (Pusan National University)


    Course description:

    Long-term observation data is an important basis for understanding the succession of ecosystems. With the gradual accumulation of instrumental data, there is an increasing demand for long-term observation data analysis, for instance, studies on the impacts of climate change on types of ecosystems. Hence, one of the main purposes of this course is to understand the law of species succession and its relationship with environmental drivers by analyzing long-term observations. The analysis of long-term observation data mainly refers to time series observation data, including both artificial and high-frequency automatic observations, etc., as well as environmental factors and biological data. As an free and open sources programming environment, R language has been favored by more and more scholars and has become one of the main tools of biostatistics.


    Course objective:

    Through this course of study, students can understand the basic theory of long-term monitoring data analysis and the way to achieve it in R. Including trend analysis, ordination analysis, random forest analysis, relative importance analysis, direct and indirect interaction analysis, and so on. The major focus of this course is the long-term changes of phytoplankton community and the impacts of climate change on species succession.


    Recommended reading or materials

    - Ovaskainen, O., Tikhonov, G., Norberg, A., Guillaume Blanchet, F., Duan, L., Dunson, D., Roslin, T., & Abrego, N. (2017).
      How to make more out of community data? A conceptual framework and its implementation as models and software.
      Ecology Letters, 20(5), 561-576.
    - Tippmann, S. (2015). Programming tools: Adventures with R. Nature, 517(7532), 109-110.
    - Deng, J. M., Paerl, H. W., Qin, B., Zhang, Y., Zhu, G., Jeppesen, E., Cai, Y., & Xu, H. (2018).
      Climatically-modulated decline in wind speed may strongly affect eutrophication in shallow lakes. Science of the Total
      Environment, 645, 1361-1370.
    - Deng, J. M., Salmaso, N., Jeppesen, E., Qin, B. Q., & Zhang, Y. L. (2019). The relative importance of weather and nutrients
      determining phytoplankton assemblages differs between seasons in large Lake Taihu, China. Aquatic Sciences, 81(3), 48.
    - Deng, J. M., Zhang, W., Qin, B. Q., Zhang, Y. L., Paerl, H. W., & Salmaso, N. (2018).
      Effects of climatically-modulated changes in solar radiation and wind speed on spring phytoplankton community dynamics
      in Lake Taihu, China. PLoS ONE, 13(10), e0205260.
    - Deng, J. M., Zhang, Y. L., Qin, B. Q., & Shi, K. (2015).
      Long-term changes in surface solar radiation and their effects on air temperature in the Shanghai region. International
      Journal of Climatology, 35(12), 3385-3396.
    - Wang, J., Pan, F., Soininen, J., Heino, J., & Shen, J. (2016).
      Nutrient enrichment modifies temperature-biodiversity relationships in large-scale field experiments.
      Nature Communications, 7, 13960.
    - Salmaso, N. (2010). Long-term phytoplankton community changes in a deep subalpine lake: responses to nutrient
      availability and climatic fluctuations. Freshwater Biology, 55(4), 825-846.
    - Jacobson, P. C., Hansen, G. J. A., Bethke, B. J., & Cross, T. K. (2017).
      Disentangling the effects of a century of eutrophication and climate warming on freshwater lake fish assemblages.
      PLoS ONE, 12(8), e0182667.
    - Olli, K., Klais, R., Tamminen, T., Ptacnik, R., & Andersen, T. (2011).
      Long term changes in the Baltic Sea phytoplankton community. Boreal Environment Research, 16, 3-14.
    - Oksanen, J., Blanchet, F. G., Friendly, M., Roeland Kindt, Legendre, P., McGlinn, D., Minchin, P. R.,
    - O'Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Eduard Szoecs, & Wagner, H. (2017).
      Package 'vegan'. Community ecology package, version 2.4-5, 2(9).
    - Wood, S. N. (2006). Generalized additive models: an introduction with R. Boca Raton: Chapman & CRC press.


    Maximum number of participants: 20 people
  • Organizer/Lecturer:

    - Dr. Song Qian (Department of Environmental Sciences , The University of Toledo)


    Course description:

    The short course covers applications of Bayesian statistics in selected environmental and ecological science fields. Case studies prepared for the short course include water quality monitoring and assessment, statistical calibration in the chemical measurement process, modeling of lake eutrophication, ecological modeling in population and community analysis, drinking water safety assessment, risk assessment of invasive species, and fishery ecology. Instead of covering statistical methods in sequence, the short course emphasizes the process of model formulation, parameter estimation, and model assessment. The focus of the short course is the process of linking ecological science and statistics for developing scientifically meaningful models. Each case study includes its scientific background and the nature of the data to articulate why the proposed model is most appropriate and what were the alternative models considered. These case studies highlight some conceptual difficulties in Bayesian statistics (e.g., the prior). In addition to models for different types of data, the short course also covers modern Bayesian computation using R and Stan.


    Annotated computer code will be distributed during the class and posted on GitHub.


    Outline:
    • Philosophical considerations
      - Box (1983) An apology for ecumenism in statistics
    • Bayes as the hero of WWII, cold war, and science
      - McGrayne (2011) The Theory That Would Not Die.
    • Introductory examples
      - An imperfect test - Monte Carlo simulation - Integrating uncertainty
    • Bayesian inference basics -- what does she mean when McGrayne says "He/She used Bayes" in her book?
    • Case studies
      - Normal response models - Count response variables - Large scale aggregation
    • Prior as a distribution across exchangeable units
      - A personal (and normative) definition of the Bayesian prior
    Recommended reading or materials

    - McGrayne, S.B. (2011) The theory that would not die: how Bayes' rule cracked the enigma code, hunted down Russian
      submarines and emerged triumphant from two centuries of controversy. Yale University Press
    - Box, G.E.P. (1983) An apology for ecumenism in statistics. In, Box, G.E.P.; Leonard, T.; and Wu, C.F (eds.) Scientific Inference,
      Data Analysis, and Robustness. Academic Press.
    - Efron, B. and Morris, C. (1977) Stein's paradox in statistics. Scientific American. 236(5), 119-127.


    Maximum number of participants: 25 people
  • Organizer/Lecturer:

    - Dr. Tae-Soo Chon (Ecology and Future Research Institute and Pusan National University (Prof. Emer.)


    Course description:

    Due to rapid industrialization and climate changes biodiversity is at stake, causing unexpected invasion/extinction of species in ecosystems. Considering complexity of population dispersal, difficulty of experiments (e.g., safety) and insufficiency of real data, simulation is desired to objectively address population dispersal mechanism to monitor and provide effective management strategies. ¡®Space¡¯ and ¡®individual¡¯ are two essential concepts in simulating ecological phenomena. Spatial approach draws attention since 1) point data have been accumulated to be extrapolated to space data through cumulative surveys, 2) spatial heterogeneity is a key issue in expressing complexity of ecological function/structure, and 3) spatial management is critical in both administrative (e.g., area-based monitoring/management) and technical (e.g., spatial structure) aspects. Individual based model is an efficient simulation tool regarding 1) precise ecological variability is presented at the individual level, 2) individual information (e.g., behavior) could be extrapolated to integrative understanding of individual-population relationships, 3) experimental data could be feasibly linked to model parameters, and 4) computation environment (e.g., computing time) has been improving rapidly for calculating individual life events in simulation. In this course we introduce how models could be developed to present population dispersal (e.g., entering, establishment, proliferation) in ecosystems through basic life processes (e.g., reproduction, movement, death) in spatiotemporal domain along with basic example programs of simulation.


    Course objective:

    The lecture will be focused on ¡®In what objectives I could apply either space or individual oriented model to ecological events?¡¯ and ¡®how the models could be utilized further to make prognosis under various scenarios for monitoring?¡¯ The course consists of two parts. The first part (1 - 2 hours) provides introductory lecture regarding brief introduction of spatial and individual-based models, model structure and characteristics of two models. The second part (2 - 3 hours) is for practicing simulation programs. Models including spatially explicit model (SEM) (e.g., cellular automata) and individual based model (IBM, or agent-based model) are demonstrated for simulation. The participants will experience the following steps regarding modeling procedure.
    1) Data treatment
    2) System definition and parameter preparation
    3) Model development with computer languages given input data
    4) Model evaluation with output data


    Hand-on practice:

    The example programs of SEM and IBM will be provided by the lecture.
    1) Given the field data as an example, the modelling system (e.g., system size, unit) will be defined, and parameter values and initial conditions are determined for modelling.
    2) Two simple models for SEM and IBM will be constructed for example cases with assistance by teaching staffs.
    3) The models will be run in different scenario for monitoring. The commonness and difference between two models will be discussed regarding mechanical processes and provision of prognosis/prediction.
    The participants are encouraged to prepare their own computers for practice. Some experience of computer language (e.g., Python) would be recommended but are not necessary.


    Recommended reading or materials

    Chon, T.-S., Lee, S. H., Jeoung, C., Cho, H.K., Lee, S.H. & Y.-J. Chung. 2008. Individual-based models. Ecological handbook of ecological modelling and informatics (Eds Jorgensen S. E., Chon, T.-S., Recknagel, F.). WIT, Southampton, UK. pp 99 - 114
    DeAngelis, D. L. and L. J. Gross. 1992. Individual-based models and approaches in ecology: populations, communities and ecosystems. Chapman and Hall, New York. 525 pp.
    Ermentrout, G.B., Edelstein-Keshet, L., 1993. Cellular automata approaches to biological modelling. J. Theor. Biol. 160, 97-133.
    Hogeweg, P. 1988. Cellular automata as a paradigm for ecological modeling. Applied Mathematics and Computation, 27: 81-100.
    Grimm, V. & Railsback, S.F., 2005. Individual-Based Modeling and Ecology, Princeton University Press: Princeton, NJ.
    Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S.K., Huse, G., Huth, A., Jepsen, J.U., J©ªrgensen, C., Mooij, W.M., Muller, B., Pe¡¯er, G., Piou, C., Railsback, S.F., Robbins, A.M., Robbins, M.M., Rossmanith, E., Ruger, N., Strand, E., Souissi, S., Stillman, R.A., Vabo, R., Visser, U. & DeAngelis, D.L.. 2006. A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 198, pp. 115-126
    Lee, S.D., Park, S., Park, Y.-S., Chung, Y.-J., Lee, B.-Y. and Chon, T.-S. 2007. Range expansion of forest pest populations by using the lattice model. Ecol. Model. 203, 157-166
    Lett, C., Silber, C. & Barret, N., 1999. Comparison of a cellular automata network and an individual-based model for the simulation of forest dynamics. Ecological Modelling, 121, pp. 277-293.
    Mazzucco, R., Nguyen, T. V., Kim, D.-H., Chon, T.-S. and Dieckmann, U. 2015. Adaptation of aquatic insects to the current flow in streams. Ecol. Model. 309 - 319, 143 - 153
    Nguyen, T. V., Park, Y.-S., Jeoung, C.-S., Choi, W.-I., Kim, Y.-K., Jung, I.-H., Shigesada, N., Kawasaki, K., Takasu, F., and Chon, T.-S. 2017. Spatially explicit model applied to pine wilt disease dispersal based on host plant infestation. Ecol. Model. 353, 54 - 62
    Wolfram. S., 1994, Collected Papers in Cellular Automata and Complexity. Addison-Wesley.
    - Other information on the lecture and practice will be announced later.


    Maximum number of participants: 25 people
  • Organizer/Lecturer:

    - Prof. Friedrich Recknagel (University of Adelaide),


    Course description:

    Evolutionary algorithms are sophisticated tools for modelling complex ecological data. By simulating the mechanisms of evolution ¡®mutation¡¯, ¡®cross-over¡¯ and ¡®reproduction¡¯, they discover the ¡°fittest¡± IF-THEN-ELSE-models among tens-of-thousands of model generations from ecological time series. Resulting ¡°fittest¡± models prove to be highly: (1) predictive e.g. for short-term forecasting and early warning of extreme population growth, as well as (2) explanatory e.g. by determining tipping-points for extreme changes at population, community or ecosystem level, and by quantifying phenological asynchrony in plankton communities in response to global change scenarios.


    Course objective:

    Teaching practical skills for data preparation and applications of the hybrid evolutionary algorithm HEA that enable participants of predictive and explanatory modelling of lake and river ecosystems.
    Demonstrating applications of HEA models for: early warning of cyanobacteria blooms, meta-analysis of cyanobacteria across lakes with different climates and trophic states, and quantification of phenological asynchrony in plankton communities in response to climate and eutrophication scenarios.


    Hand-on practice:

    Demonstrating interactively a modelling case study: (1) preprocessing long-term ecological time series of a lake, (2) designing modelling experiments by HEA, (3) communicating modelling experiments with supercomputer, (4) analyzing and documenting modelling results including model validation, threshold visualization and sensitivity analysis.


    Recommended reading or materials

    - Recknagel, F. and W. Michener (eds.), 2018. Ecological Informatics. Data management and knowledge discovery. 3rd Edition.
      Springer International, 1-482.
      (https://link.springer.com/chapter/10.1007/978-3-319-59928-1_1/fulltext.html)
    - Recknagel, F., Adrian, R. and J. Kohler, 2021. Quantifying phenological asynchrony of phyto- and zooplankton in response to   changing temperature and nutrient conditions in Lake Muggelsee (Germany) by means of evolutionary computation.   Environmental Modelling and Software (submitted)
    - Recknagel, F., Tamar Zohary, T., Rucker, J, .Orr, P., Castelo Branco, Ch.and B. Nixdorf, 2019. Causal relationships of   Raphidiopsis (formerly Cylindrospermopsis) dynamics with water temperature and N:P-ratios: A meta-analysis across lakes   with different climates based on inferential modelling. Harmful Algae 84, 222-232. https://doi.org/10.1016/j.hal.2019.04.005
    - Recknagel, F., Orr, P., Bartkow, M., Swanepoel, A. and H. Cao, 2017. Early warning of limit-exceeding concentrations of   cyanobacteria and cyanotoxins in drinking water reservoirs by inferential modelling. Harmful Algae 69, 18-27.   https://doi.org/10.1016/j.hal.2017.09.003
    - Recknagel, F., Kim, D.K., Joo, G.-J. and H. Cao, 2017. Response of Microcystis and Stephanodiscus to alternative flow regimes   of the regulated River Nakdong (South Korea) quantified by model ensembles based on the hybrid evolutionary algorithm   (HEA). River Research and Applications 33, 949-958. DOI: 10.1002/rra.3141
    - Recknagel, F., Adrian, R., Kohler, J. and H. Cao, 2016. Threshold quantification and short-term forecasting of Anabaena,   Aphanizomenon and Microcystis in the polymictic, eutrophic Muggelsee (Germany) by inferential modelling using the hybrid   evolutionary algorithm HEA. Hydrobiologia 778, 61-74. DOI 10.1007/s10750-015-2442-7


    Maximum number of participants: 25 people
  • Organizer/Lecturer:

    - Dr. Annette Janssen (Wageningen University & Research)
    - Dr. Robert Ladwig (University of Wisconsin-Madison)


    Course description:

    Aquatic ecosystem models are representations of real-world processes in aquatic ecosystems. These models provide the opportunity to explore scientific and engineering ideas or scenarios, and increase our scientific understanding of aquatic systems. Model simulations can also be used as experiments that are not possible to field-test for physical, logistical, political or financial reasons.
    In this short course, we will provide a general introduction about numerical modeling. To illustrate numerical modelling we will present two lake ecosystem models with different methodological approaches: the one-dimensional ecosystem model PCLake and the coupled vertical one-dimensional hydrodynamic-ecological model GLM-AED2.
    The aim of this course is to give a general introduction about numerical modeling of aquatic ecosystems. In the introduction, we will focus on modeling basics (mathematical description of hydrodynamics and ecological processes, features of both models). Afterwards, we will split the group into two groups that work on hands-on exercises with PCLake or GLM-AED2, respectively.

    PCLake (works on Windows):
    - Setting up PCLake
    - Run model PCLake and interpret results
    - Simulate scenarios for management

    GLM-AED2 (works on Windows, macOS and Linux):
    - Setting up a lake model (hypsography, driver data, initial data)
    - Running, calibrating and visualizing the results using R (R-Studio)
    - Coupling hydrodynamic model to water quality model

    For the hands-on exercises, we kindly ask you to bring your own laptop with the following prerequisites: Windows (needed for PCLake), R (>= 3.0) and RStudio. Moreover, we ask you to install the software before the course. We will send out additional specific instructions before the workshop.


    Recommended reading or materials:

    General:
    Jackson et al. (2000): https://academic.oup.com/bioscience/article/50/8/694/243278
    Janssen et al. (2015): https://link.springer.com/article/10.1007%2Fs10452-015-9544-1

    R
    https://cran.r-project.org/bin/windows/base/

    Rtools
    https://cran.r-project.org/bin/windows/Rtools/

    For GLM:
    website: http://aed.see.uwa.edu.au/research/models/GLM/
    Hipsey et al. (2019): https://www.geosci-model-dev.net/12/473/2019/

    For PCLake:
    Website: https://github.com/pcmodel
    Janse et al. (2008): https://www.sciencedirect.com/science/article/pii/S0075951108000315
    Janssen et al. (2019): https://www.sciencedirect.com/science/article/pii/S0304380019300201


    Maximum number of participants: 40 people
  • Organizer/Lecturer:

    - Dr. Marieke Frassl (German Federal Institute of Hydrology (BfG))


    Course description:

    Finally! You have finished your experiment, field study or modelling activity. The results are amazing! You sum up your data in several scatter plots, a few bar charts and a data table. When you present the results to your colleagues, all you see is large question marks in their faces. Only a longer discussion helps to resolve the situation.


    Course objective:

    This workshop will introduce different ways to visualize your data and it will touch on data storytelling. The aim is to equip you with a toolkit to get the best out of your data and to find the best ways of getting your message across. This will help you to write better papers, prepare gripping presentations and engage stakeholders in your research.


    Hand-on practice:

    We will talk through different data storytelling techniques with a focus on visualization. Then we will get our hands on R to run through different visualization examples. In small group activities, we will develop ideas to visualize data from your experiments. The workshop will also provide you with plenty of resources to refer to later on.
    Please write your contents here.


    Recommended reading or materials

    - will be added later


    Maximum number of participants: 20 people

More Information

- For further information about short courses, please contact us through Dr. Ji Yoon KIM (e-mail: tapegrass.kim@gmail.com) or SIL2021 office (sil2021@sil2021.org).

    KEY DATES
    D-107 22 AUGUST, 2021

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35th Congress of the International Society of Limnology (SIL2021)
426, Gonghang-daero, Gangseo-gu, Seoul, Korea (07654)
Korean Society of Limnology / Registration No. : 204-82-04773 / Representative : Sin Gyeong Hun
Contact Information : sil2021@sil2021.org
Copyright (c) Korean Society of Limnology. All Rights Reserved.

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